Artificial Intelligence (IT-504) - B Tech RGPV AICTE Flexible Curricula Notes

Friday, July 5, 2019

Artificial Intelligence (IT-504)
 B.Tech RGPV notes AICTE flexible curricula Bachelor of technology

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B.Tech RGPV notes AICTE flexible curricula Bachelor of technology

Course Objectives

 To present an overview of artificial intelligence (AI) principles and approaches
 Develop a basic understanding of the building blocks of AI


Syllabus

UNIT 1:
Meaning and definition of artificial intelligence, Production systems, Characteristics of production systems, Study and comparison of breadth first search and depth first search techniques, other Search Techniques like hill Climbing, Best first Search. A* algorithm, AO* algorithms etc, and various types of control strategies.

UNIT 2:
Knowledge Representation, Problems in representing knowledge, knowledge representation using propositional and predicate logic, comparison of propositional and predicate logic, Resolution, refutation, deduction, theorem proving, inferencing, monotonic and non-monotonic reasoning.

UNIT 3:
Probabilistic reasoning, Baye's theorem, semantic networks, scripts, schemas, frames, conceptual dependency, fuzzy logic, forward and backward reasoning.

UNIT 4:
Game playing techniques like minimax procedure, alpha-beta cut-offs etc, planning, Study of the block world problem in robotics, Introduction to understanding, natural language processing.

UNIT 5:
Introduction to learning, Various techniques used in learning, Introduction to neural networks, applications of neural networks, common sense, reasoning, some example of expert systems.


NOTES


Books Recommended

1 Rich E and Knight K, “Artificial Intelligence”, TMH, New Delhi.
2 Nelsson N.J., “Principles of Artificial Intelligence”, Springer Verlag, Berlin.


Books Recommended

Upon successful completion of this course the students will:
 Be familiar with terminology used in this area
 Explain what constitutes "Artificial" Intelligence and how to identify systems with Artificial Intelligence.
 Know how to build simple knowledge-based systems
 Have ability to apply knowledge representation, reasoning, and machine learning techniques to realworld problems


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